Data Analysis
The data analysis used for this evaluation is as follows:
Pair T test A paired t-test compares two scores from the same group of people— for example:
• Before and after a workshop
• Survey at the start and the end of a programme
• Baseline vs. follow-up responses
This is suitable for this invention as a questionnaire was asked in Workshop 1( pre) and the end of workshop 2( post). We use this as a measure to see if the students answers changed in a meaningful way. It tells us whether the difference between the measurements I real and not just random. If the p-value is small(< 0.05), it suggests a statistically significant change.
Cohen’ s D Cohen’ s d is a measure of effect size— basically, it tells you how big the difference is, not just whether it exists.
Because statistical tests( like t-tests) can sometimes show“ significant” results even when the actual change is tiny, Cohen’ s d helps you see whether the change actually matters in real life. The t-test tells us a difference exists, but the Cohen’ s d tells us how impressive that difference is.
• Small effect( around 0.2): a small but noticeable change
• Medium effect( around 0.5): a meaningful difference
• Large effect( 0.8 or more): a substantial impact
Pearson Correlation Pearson’ s correlation looks at the relationship between two continuous variables. To tell us whether the two points are connected ad how strongly.
For example:
• Does confidence increase when knowledge increases?
• Do students who attend more events score higher on engagement?
• Is satisfaction linked with likelihood to recommend?
Correlation produces a number between – 1 and + 1:
• + 1 = perfect positive relationship( as one increases, the other increases)
• 0 = no relationship
• – 1 = perfect negative relationship( as one increases, the other decreases)
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